Visão Geral

Este curso aborda metodologias, ferramentas e práticas para avaliação e benchmarking de Large Language Models (LLMs) em ambientes corporativos. O participante aprenderá a medir qualidade, precisão, segurança, desempenho, custo e confiabilidade de modelos de linguagem, além de desenvolver frameworks de avaliação para comparar diferentes modelos, prompts, arquiteturas RAG e aplicações baseadas em IA Generativa. O curso também explora métricas quantitativas e qualitativas, avaliação humana, testes automatizados e monitoramento contínuo da qualidade dos modelos.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os princípios da avaliação de Large Language Models
• Definir métricas adequadas para diferentes cenários de negócio e tecnologia
• Implementar processos de benchmarking entre modelos, prompts e arquiteturas
• Avaliar precisão, segurança, custo, desempenho e qualidade das respostas geradas por IA
• Construir pipelines de avaliação contínua para aplicações baseadas em LLMs
• Aplicar boas práticas de governança e validação de modelos em ambientes corporativos

Publico Alvo

• Engenheiros de IA e Machine Learning
• Engenheiros LLMOps e MLOps
• Cientistas de Dados
• Arquitetos de Soluções de IA
• Profissionais de Qualidade e Governança de IA
• Líderes técnicos responsáveis por plataformas de IA Generativa

Pre-Requisitos

• Conhecimentos básicos de Large Language Models
• Familiaridade com Prompt Engineering e IA Generativa
• Conhecimentos de análise de dados e métricas de desempenho
• Experiência com desenvolvimento ou operação de aplicações de IA é recomendada

Conteúdo Programatico

Module 1: Introduction to LLM Evaluation

1. Fundamentals of model evaluation
2. Importance of benchmarking in Generative AI
3. Evaluation lifecycle
4. Enterprise evaluation requirements
5. Common challenges and pitfalls
6. Overview of evaluation frameworks

Module 2: Evaluation Metrics Fundamentals

1. Accuracy and correctness metrics
2. Relevance and completeness measures
3. Consistency evaluation
4. Robustness assessment
5. Reliability indicators
6. Metric selection strategies

Module 3: Automated Evaluation Techniques

1. Rule-based evaluation approaches
2. LLM-as-a-Judge methodologies
3. Reference-based evaluation
4. Semantic similarity techniques
5. Automated scoring systems
6. Evaluation automation frameworks

Module 4: Human Evaluation Methodologies

1. Human-in-the-loop evaluation
2. Expert review processes
3. Annotation methodologies
4. Evaluation rubrics
5. Inter-rater agreement concepts
6. Quality assurance workflows

Module 5: Benchmarking Large Language Models

1. Model comparison methodologies
2. Public benchmark analysis
3. Enterprise benchmark design
4. Comparative testing frameworks
5. Benchmark datasets
6. Performance interpretation techniques

Module 6: Prompt and Response Evaluation

1. Prompt quality assessment
2. Prompt comparison strategies
3. Response scoring techniques
4. Structured output validation
5. Hallucination detection methods
6. Prompt optimization workflows

Module 7: Evaluating RAG Architectures

1. RAG evaluation fundamentals
2. Retrieval quality assessment
3. Context relevance analysis
4. Groundedness evaluation
5. Knowledge accuracy validation
6. End-to-end RAG benchmarking

Module 8: Safety and Security Evaluation

1. Harmful content assessment
2. Bias and fairness evaluation
3. Prompt injection testing
4. Adversarial evaluation techniques
5. Data leakage detection
6. AI safety benchmarking

Module 9: Performance and Cost Benchmarking

1. Latency measurement
2. Throughput evaluation
3. Token utilization analysis
4. Cost-performance optimization
5. Scalability assessment
6. Infrastructure benchmarking

Module 10: Continuous Evaluation and Monitoring

1. Production evaluation strategies
2. Drift detection techniques
3. Continuous quality monitoring
4. Alerting and reporting mechanisms
5. Operational dashboards
6. Evaluation lifecycle management

Module 11: Governance and Compliance Validation

1. AI governance frameworks
2. Regulatory evaluation requirements
3. Auditability principles
4. Compliance assessment workflows
5. Risk management integration
6. Responsible AI validation

Module 12: LLM Evaluation and Benchmarking Workshop

1. Model benchmarking laboratory
2. Prompt evaluation exercises
3. RAG assessment projects
4. Safety and performance testing
5. Continuous evaluation pipeline implementation
6. Final enterprise LLM evaluation and benchmarking project